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2016-02-25 11:03:41
Michael Schrage
February 04, 2016
Counting calories and steps may be terrific for improving personal health. But
what counts most in promoting professional success? Quantifying the business
self is an essential precursor for enterprise networks that empower people to
manage their strengths and weaknesses better, faster, and cheaper.
The personal productivity future is clear: Anybody and everybody who wants to
succeed in tomorrow s 21st Century organizations will have to commit to levels
of self-monitoring, self-surveillance, and self-quantification that makes
Orwell read like Pollyanna. The reason isn t post-industrial intrusiveness or
invasiveness but an imperative for professional self-preservation and
self-improvement.
Don t think Big Brother, think Big Data-Driven Coach.
The best chance knowledge workers have to cost-effectively compete with smart
(er) machines is by embracing technologies to become smarter and more
influential themselves. The typical American office worker reportedly processes
well over 5,000 megabytes a day. That number is rising. Failure to convert
rising data tides into greater personal productivity is an invitation to
unemployment. This will hold as true for Indian and Chinese knowledge workers
as their OECD counterparts. This trend is as global as it gets.
In other words, workplace data that don t cost-effectively augment human
performance will likely be used to automate it. For world-class organizations,
tomorrow s quantified self hurtles beyond constant self-assessment to
relentless self-improvement. If you re not getting measurably better, you re
going to go away.
Where self-tracking/monitoring tools, not unlike magnification mirrors, are
used primarily to let people literally see themselves in new light and higher
resolution, these technologies will also recommend the best and most realistic
options for improving performance.
Call it the enterprise Amazon-ification or Netflix-ification of quantified
self-help. Much the way Amazon suggests books to read and Netflix recommends
videos to binge watch, data-driven digital firms will aggregate, synthesize,
and customize explicit recommendations designed to make their people productive
and effective. More sophisticated recommenders will proffer advice to
stimulate creativity and collaboration. Innovative leaders will algorithmically
invest in optimizing human performance, as well as process efficiencies.
By passively monitoring chats, emails, memos, and presentations, for example,
enterprise recommenders know individual communications patterns and styles.
So as managers draft critical project reviews, the software like an
emotional autocorrect could suggest word choices to make those criticisms
affectively effective. Managers are, of course, free to accept or reject those
data-driven edits, but the recommender will, of course, monitor their choices
and the concomitant results. That s how recommenders learn.
Depending upon their level of personalization, recommenders can prompt
introverts to use enterprise social media more effectively and encourage
overly-aggressive communicators to throttle back. Specific memos to read,
colleagues to invite, and meetings to skip could all fall within the
algorithmic purview of these data-driven advisory regimes.
Consequently, getting and staying on the career fast track will require the
humility and self-discipline to follow the best advice of the smartest machines
you can find. Professional success may be contingent upon trusting these
technologies as much or more than one s colleagues.
The managerial and organizational implications are, of course, enormous. They
re anticipated in the growing people analytics movement that calls for
leadership at all levels to rigorously define and measure what excellence
should mean. The critical difference now clearly emerging is that,
increasingly, the best dispensers of advice and counsel may be the machines
themselves and not the ostensibly human leadership.
Indeed, one of the real risks and opportunities for this next-generation of
enterprise recommenders is that top management will increasingly depend upon
these technologies to understand their (very) human colleagues. The line
between reliance and dependence becomes vanishingly small. That said, what
should managers do when the recommenders they rely on do a measurably better
job than their own human intuitions and insights?
That s going to be the challenge haunting top managers in tomorrow s digital
enterprises. Sometimes, it s not just what you do that counts, but how you
count what you do.
Michael Schrage, a research fellow at MIT Sloan School s Center for Digital
Business, is the author of the books Serious Play (HBR Press), Who Do You Want
Your Customers to Become? (HBR Press) and The Innovator s Hypothesis (MIT
Press).